Daily activity forecasts play an important role in the daily lives of residents in smart homes. Category forecasts and occurrence time forecasts of daily activity are two key tasks. Category forecasts of daily activity are correlated with occurrence time forecasts, however, existing research has only focused on one of the two tasks. Moreover, the performance of daily activity forecasts is low when the two tasks are performed in series. In this paper, a forecast model based on multi-task learning is proposed to forecast category and occurrence time of daily activity mutually and iteratively. Firstly, raw sensor events are pre-processed to form a feature space of daily activity. Secondly, a parallel multi-task learning model which combines a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) units are developed as the forecast model. Finally, five distinct datasets are used to evaluate the proposed model. The experimental results show that compared with the state-of-the-art single-task learning models, this model improves accuracy by at least 2.22%, and the metrics of NMAE, NRMSE and R2 are improved by at least 1.542%, 7.79% and 1.69%, respectively.
With the benefits to improve productivity and reduce operation cost, RFID (Radio Frequency Identification) has recently seen a great increase in a wide variety of business information systems. However, the downside in business applications is the problem caused by data security and privacy. Enhancement without extra costs in RFID identification poses new challenges to privacy and security in RFID-driven just-in-time business information systems. In this paper, we propose an unilateral randomly authentication protocol on the basis of one-way hash function for low-cost RFID tags. We illustrate the whole operating procedure of the proposed protocol in a typical RFID system. Experimental results show that the proposed protocol has some security improvements in data consistency and can work against some attacks like eavesdropping and DoS, compared with several existing methods.
Abstract. The dynamic changing feature of Semantic Web determines that the ontology which is a part of Semantic Web needs constantly to be modified in order to adapt outer environment. In this paper we make a careful analysis of the ontology changes' complexity under open environment. The main contents discussed are as follow. At first we point out all possible relation types between any two ontology change sequences including directly conflict relation, indirectly conflict relation, dependent relation and compatible relation according to ontology change's definition. And then we propose a new algorithm named Algorithm of Searching Maximum and Sequential Ontology Change Sequence Set(ASMSOCSS) to find all maximum and sequential ontology change sequence subset in the prime ontology change sequence set and prove the independence of the result which may be got after running ASMSOCSS. At last we put forward the algorithm by using these maximum and sequential ontology change sequence sets to create new ontology versions according to the dependence relation between ontology change sequences.
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